1. Field
Aspects of the present invention generally relate to methods and devices for processing measurement spectral data obtained by measuring biological tissue, and in particular relates to a method and a device for processing image data for a multivariate analysis.
2. Description of the Related Art
Conventionally, biological tissue has been observed with a microscope, and constituent substances or contained substances associated with the observed biological tissue have been visualized. For such visualization, mass spectrometry or Raman spectroscopy is employed. As a measurement spectrum, a mass spectrum, an ultraviolet, visible, or infrared optical spectrum, and so on are used. With such a measuring method, information on a spatial distribution of peak values in the measurement spectrum associated with the measured substance can be obtained, and thus a spatial distribution of the substance contained in the biological tissue associated with the measurement spectrum can be obtained.
With mass spectrometry, the time of flight of an electrically charged ion depends on mass m of the ion and an electric charge z. On the basis of the above, the ion can be identified, and a mass spectrum at each point on the sample can be obtained.
With Raman spectroscopy, a light source irradiates a substance with monochromatic laser light, and generated Raman scattered light is detected with a spectrometer or an interferometer so as to obtain a Raman spectrum. A difference between the frequency of the Raman scattered light and the frequency of the incident light (i.e., Raman shift) takes a value unique to the structure of the substance, and thus a Raman spectrum unique to the measured substance can be obtained.
To date, a multivariate analysis, in which intensity information of a broad wavelength band is handled as a variate, has been employed to analyze measurement spectral data. According to a principal component analysis (PCA) or an independent component analysis (ICA), which are types of the multivariate analysis, even with a complicated spectrum in which vibration spectra or band structures of components contained in a biological sample are superimposed on one another, the chemical state of the biological sample can be classified and measured. As an example, according to Japanese Patent Laid-Open No. 2011-174906, a PCA is carried out on an optical spectrum of each pixel to obtain a distribution of principal component scores, and thus morphologic information or composition of a biological sample is examined.
When a PCA is carried out, a sample variance-covariance matrix is obtained, and an eigenvalue and an eigenvector of the sample variance-covariance matrix are then obtained. A sample variance-covariance matrix, however, contains data having a size of a spectral number by a spectral number. Thus, when a spectral number used in an analysis is large or when a large number of pieces of image data are to be handled, the data amount increases, disadvantageously leading to an increased processing time.